The Real Economics of AI Copilots: Why Cost Per Query Will Define the Winners

“AI built in” is the new normal in SaaS. Nearly every SaaS platform now offers an assistant promising productivity gains, automation, and intelligent recommendations. But beneath the headlines and subscription price debates lies a much more consequential metric — one that ultimately determines which companies thrive and which quietly erode their margins. Behind the scenes, cost per query determines the financial viability of AI.

As a CEO building AI directly into a CRM platform, I can tell you this: if you don’t understand your unit economics at the query level, you don’t understand your AI business.

Why Cost Per Query Matters More Than Subscription Pricing

The market conversation often centers on whether AI should be bundled into existing plans or sold as an add-on. But that’s a surface-level debate. The deeper issue is whether your economics support your strategy.

I often compare it to hotel WiFi. There was a time when hotels charged extra for internet access. Today, that seems absurd. Why? Because connectivity became essential to the experience — as fundamental as a  bed or a shower.

AI copilots are heading in the same direction. They won’t remain optional bolt-ons forever. They’ll become embedded expectations within core software experiences, and your pricing needs to reflect that.

While engineering the Insightly CRM Copilot, we knew early on that if AI was going to meaningfully enhance sales pipeline management, contact management, and forecasting, it couldn’t introduce margin pressure. That meant driving cost per query low enough to support inclusion in existing CRM plans — not treating AI as a luxury surcharge.

Some vendors have chosen the add-on model. That’s a defensible strategy. But we’re building for the long game. We want CRM users to embrace the technology and have it make their CRM usage experience more powerful, thereby leading to customer retention. That long game requires disciplined unit economics.

The Hidden Variable in AI ROI: Model Selection

Model pricing varies dramatically based on complexity, usage patterns, and output requirements. Executive teams evaluating AI ROI should think less about model brand names and more about workload design.

I am proud to see that our engineering team approached this intentionally.

Not every AI task deserves a premium model. Lightweight, high-volume tasks — like formatting suggestions, simple summaries, or low-risk recommendations — can run on lower-cost models. More complex reasoning, multi-step generation, and customer-facing outputs require more powerful models where quality and speed materially impact outcomes.

This tiered routing strategy allows us to balance performance and reliability with cost efficiency. We pay for premium intelligence only when it is needed and when it genuinely improves results.

AI ROI doesn’t come from using the most advanced model everywhere. It comes from architectural discipline.

Usage Patterns Shape Economics

One overlooked factor in AI economics is usage variability across teams.

In sales team environments, usage differs dramatically depending on workflow. Front line sellers may use AI frequently for email copy refinement and record summarizations, whereas sales leaders may lean on it for account analysis and company leadership may interact with it for forecasts.

We monitor engagement patterns closely: daily active users, prompts per user per day, session duration, and task types. These metrics help us understand where value is being delivered, and where efficiency gaps exist.

Two organizations may pay the same per-seat price yet experience very different economic outcomes based on adoption… And adoption is everything.

AI as an Adoption Multiplier

For years, software adoption has been hindered by complexity. Too many clicks. Too many menus. Too much manual work. Conversational AI changes that equation. When users can generate insights, clean data, summarize performance, or create assets through natural language, friction drops, busy work disappears, and the platform becomes more intuitive.

In our research, teams that fully adopt their CRM consistently report significantly higher efficiency gains; in turn, their vendors report lower churn. AI copilots accelerate and enhance customer adoption by removing barriers to engagement.

The real value of AI isn’t novelty. It’s reducing friction so the platform can finally deliver on its promise.

Architectural Principles That Protect Margins

Embedding AI responsibly requires deliberate engineering choices.

From day one, we focused on:

• Task-based model routing —

Matching model capability to task complexity.

• Prompt optimization —

Refining instructions to reduce token waste while preserving quality.

• Response caching —

Avoiding redundant computation where possible.

• Ongoing model benchmarking —

Evaluating quality thresholds, latency requirements, and cost-per-task targets before expanding usage.

Model selection is not a one-time decision. It’s an ongoing optimization exercise.

Today’s AI is constantly evolving with pricing shifts, evolving capabilities, and changing workloads. As a software provider, your architecture must adapt continuously. The result is improved unit economics and strategic flexibility. We avoid over-reliance on a single provider, reduce cost volatility, and maintain the ability to adopt better models as the market evolves.

Also Read: AiThority Interview With Arun Subramaniyan, Founder & CEO, Articul8 AI

How Software Companies Are Managing Financial Risk in AI Deployment

Mid-market companies — and the SaaS providers serving them — want enterprise-grade automation without financial unpredictability.

Some practical steps to mitigate AI cost risk:

1. Run an extended beta period.

Study real usage before committing to broad rollout assumptions.

2. Set thoughtful usage limits.

Generous thresholds with overage models protect margins without constraining value.

3. Diversify AI providers.

Route varying query types across multiple models to optimize cost-performance balance.

4. Continuously refine query design.

Efficiency gains compound quickly at scale.

When we began building AI capabilities, queries cost us pennies. Through optimization and multi-model strategies, we reduced that to fractions of a penny. That delta changes everything.

When Cost Per Query Becomes a Leadership-Level Conversation

AI enthusiasm is high — and rightly so. But enthusiasm doesn’t eliminate cost.

As AI usage scales, cost per query will increasingly become a vital company metric, especially for companies embedding AI into core offerings without price increases.

If you’re a software executive today, you should be asking:

• What is our true unit cost per AI interaction?
• How does that scale under peak adoption?
• Are we architected for flexibility as model pricing shifts?
• Can we absorb AI growth without margin erosion?

AI assistants and copilots are not just product features. They are economic systems embedded inside your business model. The companies that treat them as such — measuring, optimizing, and architecting intentionally — will win. And the rest may find that their most exciting innovation quietly erodes their gross margins.

Also Read: Cheap and Fast: The Strategy of LLM Cascading (Frugal GPT)

About The Author Of This Article

Steve Oriola is the CEO of Unbounce Go-to-Market Solutions. He is a tenured CEO with more than two decades of experience scaling dynamic B2B SaaS platforms, including Act!, Constant Contact, Pipedrive, and Julius. He recently led Unbounce through the acquisition of Insightly CRM where the two companies effectively merged. He served as Executive in Residence at Bessemer Venture Partners where he participated in partner meetings and evaluated investment opportunities while providing advice and counsel to portfolio companies. Steve Oriola attended Boston University Questrom School of Business.